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Part 1. Definition
In this task you will be given a list of integers. You should round each integer to the nearest tens place. That means you should round the number to the nearest multiple of 10.
Part 2. Example
[-83, 53, -48, 8]
Answer: [-80, 50, -50, 10]
Explanation: The output correctly rounds each integer in the input list to the nearest ten. So this is a good example.
Part 3. Exercise
[207, -524, 185, 667, -736, -61, -411, -479, -698, 771, 3, -688, 687, 546, -861]
Answer:
|
[210, -520, 180, 670, -740, -60, -410, -480, -700, 770, 0, -690, 690, 550, -860]
|
task373_synthetic_round_tens_place
|
NIv2
|
fs_opt
| 7
|
train
|
Detailed Instructions: In this task you will be given a list of integers. You should round each integer to the nearest tens place. That means you should round the number to the nearest multiple of 10.
See one example below:
Problem: [-83, 53, -48, 8]
Solution: [-80, 50, -50, 10]
Explanation: The output correctly rounds each integer in the input list to the nearest ten. So this is a good example.
Problem: [-911, 798, -55, 728, 411, -663, 631, -626, -895, -415, -106, -237, 146]
Solution:
|
[-910, 800, -60, 730, 410, -660, 630, -630, -900, -420, -110, -240, 150]
|
task373_synthetic_round_tens_place
|
NIv2
|
fs_opt
| 4
|
train
|
In this task you will be given a list of integers. You should round each integer to the nearest tens place. That means you should round the number to the nearest multiple of 10.
[-68, 81]
[-70, 80]
[873, -786, 329, 824, -877, -169, 398, 864, 913, -837, -78]
[870, -790, 330, 820, -880, -170, 400, 860, 910, -840, -80]
[-17, 316, -198, -267, -560, -87, 427, -931, -31, 366, 27, 211]
|
[-20, 320, -200, -270, -560, -90, 430, -930, -30, 370, 30, 210]
|
task373_synthetic_round_tens_place
|
NIv2
|
fs_opt
| 0
|
train
|
In this task you will be given a list of integers. You should round each integer to the nearest tens place. That means you should round the number to the nearest multiple of 10.
[-46, 966]
[-50, 970]
[-109, -20, 461, 670, 330, -163, -960, 965, -127, -165, -687, 53, -165, 782, -345]
[-110, -20, 460, 670, 330, -160, -960, 960, -130, -160, -690, 50, -160, 780, -340]
[-382, -174, -510, -329, -741, -157, 94, -812, -757, 115]
|
[-380, -170, -510, -330, -740, -160, 90, -810, -760, 120]
|
task373_synthetic_round_tens_place
|
NIv2
|
fs_opt
| 0
|
train
|
You will be given a definition of a task first, then some input of the task.
In this task you will be given a list of integers. You should round each integer to the nearest tens place. That means you should round the number to the nearest multiple of 10.
[-22, -472, 999, -919, -758, -544, 576, 479, 470, -106, 709, 94, -263, 120]
Output:
|
[-20, -470, 1000, -920, -760, -540, 580, 480, 470, -110, 710, 90, -260, 120]
|
task373_synthetic_round_tens_place
|
NIv2
|
zs_opt
| 1
|
train
|
Part 1. Definition
In this task you will be given a list of integers. You should round each integer to the nearest tens place. That means you should round the number to the nearest multiple of 10.
Part 2. Example
[-83, 53, -48, 8]
Answer: [-80, 50, -50, 10]
Explanation: The output correctly rounds each integer in the input list to the nearest ten. So this is a good example.
Part 3. Exercise
[0, 405, 898, 970, 269, -304, -799, 545, -79]
Answer:
|
[0, 400, 900, 970, 270, -300, -800, 540, -80]
|
task373_synthetic_round_tens_place
|
NIv2
|
fs_opt
| 7
|
train
|
instruction:
In this task you will be given a list of integers. You should round each integer to the nearest tens place. That means you should round the number to the nearest multiple of 10.
question:
[-162, 766, 719, 409, -702, -626, -404, -542, 42, -211, 624, 957]
answer:
[-160, 770, 720, 410, -700, -630, -400, -540, 40, -210, 620, 960]
question:
[-905, -388, -799, -869, -695, -774, -11, 563, 301, -62, 536, -344, 542, 713, 960]
answer:
[-900, -390, -800, -870, -700, -770, -10, 560, 300, -60, 540, -340, 540, 710, 960]
question:
[-602, 95, 934, 815, 719, -880, -233, -927]
answer:
|
[-600, 100, 930, 820, 720, -880, -230, -930]
|
task373_synthetic_round_tens_place
|
NIv2
|
fs_opt
| 9
|
train
|
Given the task definition and input, reply with output. In this task you will be given a list of integers. You should round each integer to the nearest tens place. That means you should round the number to the nearest multiple of 10.
[836, -290, 400, -697, -163, -695]
|
[840, -290, 400, -700, -160, -700]
|
task373_synthetic_round_tens_place
|
NIv2
|
zs_opt
| 5
|
train
|
Definition: In this task you will be given a list of integers. You should round each integer to the nearest tens place. That means you should round the number to the nearest multiple of 10.
Input: [-950, 79, -956, -809, 761, -669, 39, -398, -110, 13, 783, 350, 544, 59]
Output:
|
[-950, 80, -960, -810, 760, -670, 40, -400, -110, 10, 780, 350, 540, 60]
|
task373_synthetic_round_tens_place
|
NIv2
|
zs_opt
| 2
|
test
|
Part 1. Definition
In this task you will be given a list of integers. You should round each integer to the nearest tens place. That means you should round the number to the nearest multiple of 10.
Part 2. Example
[-83, 53, -48, 8]
Answer: [-80, 50, -50, 10]
Explanation: The output correctly rounds each integer in the input list to the nearest ten. So this is a good example.
Part 3. Exercise
[-234, -131, -556, -560, 784, -605]
Answer:
|
[-230, -130, -560, -560, 780, -600]
|
task373_synthetic_round_tens_place
|
NIv2
|
fs_opt
| 7
|
validation
|
Classify the input text into either disfluent questions or proper questions. A disfluent question is a question that has some interruptions in it while framing. A proper question is the correct form of the question without any disfluency.
--------
Question: When was er how did the party overthrow the elected government in 1989?
Answer: disfluent
Question: Why should one not go to jail?
Answer: proper
Question: Who has the no no I mean what happens if a member doesn't vote the party line?
Answer:
|
disfluent
|
task1623_disfl_qa_disfluent_question_classification
|
NIv2
|
fs_opt
| 7
|
train
|
Part 1. Definition
Classify the input text into either disfluent questions or proper questions. A disfluent question is a question that has some interruptions in it while framing. A proper question is the correct form of the question without any disfluency.
Part 2. Example
Who influenced the fundamentalist sorry no non-Taliban ideology?
Answer: disfluent
Explanation: The input sentence is correctly recognized as disfluent and it has words like 'sorry no' which shows disfluency hence it is a positive example.
Part 3. Exercise
What allows the adaptive immune system to react faster and more strongly each subsequent time a pathogen is encountered?
Answer:
|
proper
|
task1623_disfl_qa_disfluent_question_classification
|
NIv2
|
fs_opt
| 7
|
train
|
Teacher:Classify the input text into either disfluent questions or proper questions. A disfluent question is a question that has some interruptions in it while framing. A proper question is the correct form of the question without any disfluency.
Teacher: Now, understand the problem? Solve this instance: What type of requirement does the College's common core require?
Student:
|
proper
|
task1623_disfl_qa_disfluent_question_classification
|
NIv2
|
zs_opt
| 6
|
train
|
Detailed Instructions: Classify the input text into either disfluent questions or proper questions. A disfluent question is a question that has some interruptions in it while framing. A proper question is the correct form of the question without any disfluency.
Problem:What city, sorry, building was a gift from the Soviet Union?
Solution:
|
disfluent
|
task1623_disfl_qa_disfluent_question_classification
|
NIv2
|
zs_opt
| 8
|
train
|
Classify the input text into either disfluent questions or proper questions. A disfluent question is a question that has some interruptions in it while framing. A proper question is the correct form of the question without any disfluency.
[Q]: What is one country that can't be suggested for importation of medicines?
[A]: proper
[Q]: Which society was responsible for the publication of the joint statement?
[A]: proper
[Q]: The Rhine first formed a boundary between Gaul and what else?
[A]:
|
proper
|
task1623_disfl_qa_disfluent_question_classification
|
NIv2
|
fs_opt
| 5
|
train
|
Teacher:Classify the input text into either disfluent questions or proper questions. A disfluent question is a question that has some interruptions in it while framing. A proper question is the correct form of the question without any disfluency.
Teacher: Now, understand the problem? Solve this instance: What Chinese or no make that Khitan leader rejected the Mongols?
Student:
|
disfluent
|
task1623_disfl_qa_disfluent_question_classification
|
NIv2
|
zs_opt
| 6
|
train
|
Classify the input text into either disfluent questions or proper questions. A disfluent question is a question that has some interruptions in it while framing. A proper question is the correct form of the question without any disfluency.
One example: Who influenced the fundamentalist sorry no non-Taliban ideology?
Solution is here: disfluent
Explanation: The input sentence is correctly recognized as disfluent and it has words like 'sorry no' which shows disfluency hence it is a positive example.
Now, solve this: In what type of ring can prime ideals be used for validating quadratic reciprocity?
Solution:
|
proper
|
task1623_disfl_qa_disfluent_question_classification
|
NIv2
|
fs_opt
| 6
|
train
|
Definition: Classify the input text into either disfluent questions or proper questions. A disfluent question is a question that has some interruptions in it while framing. A proper question is the correct form of the question without any disfluency.
Input: What do I mean how do private schools in Ireland different from most?
Output:
|
disfluent
|
task1623_disfl_qa_disfluent_question_classification
|
NIv2
|
zs_opt
| 2
|
train
|
Detailed Instructions: Classify the input text into either disfluent questions or proper questions. A disfluent question is a question that has some interruptions in it while framing. A proper question is the correct form of the question without any disfluency.
Q: What percentile of gross domestic product does manufacturing comprised no sorry what percentage does construction comprise?
A:
|
disfluent
|
task1623_disfl_qa_disfluent_question_classification
|
NIv2
|
zs_opt
| 9
|
test
|
You will be given a definition of a task first, then an example. Follow the example to solve a new instance of the task.
Classify the input text into either disfluent questions or proper questions. A disfluent question is a question that has some interruptions in it while framing. A proper question is the correct form of the question without any disfluency.
Who influenced the fundamentalist sorry no non-Taliban ideology?
Solution: disfluent
Why? The input sentence is correctly recognized as disfluent and it has words like 'sorry no' which shows disfluency hence it is a positive example.
New input: What does or like why should one not go to jail?
Solution:
|
disfluent
|
task1623_disfl_qa_disfluent_question_classification
|
NIv2
|
fs_opt
| 0
|
validation
|
Instructions: The provided file includes inquiries about restaurants in Polish, and we ask you to translate those to English language. Please bear in mind the following guidelines while doing the translation: 1) We are looking for the most naturally written and formal form of each sentence in your language. We are *NOT* looking for colloquial forms of the sentence. We are looking for formal form which is how you would type your queries in a text-based virtual assistant. 2) The words between quotation marks *SHOULD NOT* be translated. We expect you to keep those values intact and include the quotation marks around them as well. 3) The fully capitalized words like DATE_0, or DURATION_0 *SHOULD NOT* be translated. Please keep them as they are in the translations. 4) Please do not localize measurement units like miles to kilometers during your translation. miles should be translated to its equivalent in your language. 6) Note the input is all lowercased except for fully capitalized special placeholders (e.g. NUMBER, DATE, TIME). Please do the same in your translations.
Input: pokaż restauracje ocenione na 2 gwiazdek.
Output:
|
show me 2 star restaurants .
|
task263_spl_translation_pl_en
|
NIv2
|
zs_opt
| 3
|
train
|
Definition: The provided file includes inquiries about restaurants in Polish, and we ask you to translate those to English language. Please bear in mind the following guidelines while doing the translation: 1) We are looking for the most naturally written and formal form of each sentence in your language. We are *NOT* looking for colloquial forms of the sentence. We are looking for formal form which is how you would type your queries in a text-based virtual assistant. 2) The words between quotation marks *SHOULD NOT* be translated. We expect you to keep those values intact and include the quotation marks around them as well. 3) The fully capitalized words like DATE_0, or DURATION_0 *SHOULD NOT* be translated. Please keep them as they are in the translations. 4) Please do not localize measurement units like miles to kilometers during your translation. miles should be translated to its equivalent in your language. 6) Note the input is all lowercased except for fully capitalized special placeholders (e.g. NUMBER, DATE, TIME). Please do the same in your translations.
Input: pokaż mi " mexican " restauracje.
Output:
|
show me " mexican " restaurants .
|
task263_spl_translation_pl_en
|
NIv2
|
zs_opt
| 2
|
train
|
Teacher:The provided file includes inquiries about restaurants in Polish, and we ask you to translate those to English language. Please bear in mind the following guidelines while doing the translation: 1) We are looking for the most naturally written and formal form of each sentence in your language. We are *NOT* looking for colloquial forms of the sentence. We are looking for formal form which is how you would type your queries in a text-based virtual assistant. 2) The words between quotation marks *SHOULD NOT* be translated. We expect you to keep those values intact and include the quotation marks around them as well. 3) The fully capitalized words like DATE_0, or DURATION_0 *SHOULD NOT* be translated. Please keep them as they are in the translations. 4) Please do not localize measurement units like miles to kilometers during your translation. miles should be translated to its equivalent in your language. 6) Note the input is all lowercased except for fully capitalized special placeholders (e.g. NUMBER, DATE, TIME). Please do the same in your translations.
Teacher: Now, understand the problem? Solve this instance: wyświetl listę restauracji " vegan " z minimalną liczbą recenzji: 0.
Student:
|
show me a list of " vegan " restaurants with at least 0 reviews .
|
task263_spl_translation_pl_en
|
NIv2
|
zs_opt
| 6
|
train
|
Given the task definition, example input & output, solve the new input case.
The provided file includes inquiries about restaurants in Polish, and we ask you to translate those to English language. Please bear in mind the following guidelines while doing the translation: 1) We are looking for the most naturally written and formal form of each sentence in your language. We are *NOT* looking for colloquial forms of the sentence. We are looking for formal form which is how you would type your queries in a text-based virtual assistant. 2) The words between quotation marks *SHOULD NOT* be translated. We expect you to keep those values intact and include the quotation marks around them as well. 3) The fully capitalized words like DATE_0, or DURATION_0 *SHOULD NOT* be translated. Please keep them as they are in the translations. 4) Please do not localize measurement units like miles to kilometers during your translation. miles should be translated to its equivalent in your language. 6) Note the input is all lowercased except for fully capitalized special placeholders (e.g. NUMBER, DATE, TIME). Please do the same in your translations.
Example: czy w pobliżu znajdują się jakiekolwiek restauracje " italian " z opiniami z 0 gwiazdką/gwiazdkami?
Output: are there any " italian " restaurants nearby with 0 star reviews ?
The translation correctly preserves " italian " entity and is accurate
New input case for you: pokaż restauracje, które zostały ocenione na powyżej 1
Output:
|
display restaurants that were rated above 1
|
task263_spl_translation_pl_en
|
NIv2
|
fs_opt
| 1
|
train
|
The provided file includes inquiries about restaurants in Polish, and we ask you to translate those to English language. Please bear in mind the following guidelines while doing the translation: 1) We are looking for the most naturally written and formal form of each sentence in your language. We are *NOT* looking for colloquial forms of the sentence. We are looking for formal form which is how you would type your queries in a text-based virtual assistant. 2) The words between quotation marks *SHOULD NOT* be translated. We expect you to keep those values intact and include the quotation marks around them as well. 3) The fully capitalized words like DATE_0, or DURATION_0 *SHOULD NOT* be translated. Please keep them as they are in the translations. 4) Please do not localize measurement units like miles to kilometers during your translation. miles should be translated to its equivalent in your language. 6) Note the input is all lowercased except for fully capitalized special placeholders (e.g. NUMBER, DATE, TIME). Please do the same in your translations.
--------
Question: szukaj " gates barbecue "?
Answer: search for " gates barbecue " ?
Question: pokaż restauracje ocenione na 2 gwiazdek.
Answer: show me 2 star restaurants .
Question: wyświetl wszystkie restauracje " chinese " z ocenami z 5 gwiazdką/gwiazdkami w odległości 5 mili.
Answer:
|
show me all " chinese " restaurants with 5 star ratings within 5 miles .
|
task263_spl_translation_pl_en
|
NIv2
|
fs_opt
| 7
|
train
|
The provided file includes inquiries about restaurants in Polish, and we ask you to translate those to English language. Please bear in mind the following guidelines while doing the translation: 1) We are looking for the most naturally written and formal form of each sentence in your language. We are *NOT* looking for colloquial forms of the sentence. We are looking for formal form which is how you would type your queries in a text-based virtual assistant. 2) The words between quotation marks *SHOULD NOT* be translated. We expect you to keep those values intact and include the quotation marks around them as well. 3) The fully capitalized words like DATE_0, or DURATION_0 *SHOULD NOT* be translated. Please keep them as they are in the translations. 4) Please do not localize measurement units like miles to kilometers during your translation. miles should be translated to its equivalent in your language. 6) Note the input is all lowercased except for fully capitalized special placeholders (e.g. NUMBER, DATE, TIME). Please do the same in your translations.
Example Input: pokaż restauracje ocenione na conajmniej 8 gwiazdki.
Example Output: show me restaurants that have a rating of at least 8 stars .
Example Input: gdzie jest " thai kitchen "?
Example Output: where is " thai kitchen " ?
Example Input: pokaż restauracje ocenione na conajmniej 8 gwiazdki.
Example Output:
|
show me restaurants that have a rating of at least 8 stars .
|
task263_spl_translation_pl_en
|
NIv2
|
fs_opt
| 3
|
train
|
The provided file includes inquiries about restaurants in Polish, and we ask you to translate those to English language. Please bear in mind the following guidelines while doing the translation: 1) We are looking for the most naturally written and formal form of each sentence in your language. We are *NOT* looking for colloquial forms of the sentence. We are looking for formal form which is how you would type your queries in a text-based virtual assistant. 2) The words between quotation marks *SHOULD NOT* be translated. We expect you to keep those values intact and include the quotation marks around them as well. 3) The fully capitalized words like DATE_0, or DURATION_0 *SHOULD NOT* be translated. Please keep them as they are in the translations. 4) Please do not localize measurement units like miles to kilometers during your translation. miles should be translated to its equivalent in your language. 6) Note the input is all lowercased except for fully capitalized special placeholders (e.g. NUMBER, DATE, TIME). Please do the same in your translations.
Let me give you an example: czy w pobliżu znajdują się jakiekolwiek restauracje " italian " z opiniami z 0 gwiazdką/gwiazdkami?
The answer to this example can be: are there any " italian " restaurants nearby with 0 star reviews ?
Here is why: The translation correctly preserves " italian " entity and is accurate
OK. solve this:
znajdź miejsca, w których można coś zjeść w "detroit".
Answer:
|
find me some places to eat in " detroit " .
|
task263_spl_translation_pl_en
|
NIv2
|
fs_opt
| 8
|
train
|
TASK DEFINITION: The provided file includes inquiries about restaurants in Polish, and we ask you to translate those to English language. Please bear in mind the following guidelines while doing the translation: 1) We are looking for the most naturally written and formal form of each sentence in your language. We are *NOT* looking for colloquial forms of the sentence. We are looking for formal form which is how you would type your queries in a text-based virtual assistant. 2) The words between quotation marks *SHOULD NOT* be translated. We expect you to keep those values intact and include the quotation marks around them as well. 3) The fully capitalized words like DATE_0, or DURATION_0 *SHOULD NOT* be translated. Please keep them as they are in the translations. 4) Please do not localize measurement units like miles to kilometers during your translation. miles should be translated to its equivalent in your language. 6) Note the input is all lowercased except for fully capitalized special placeholders (e.g. NUMBER, DATE, TIME). Please do the same in your translations.
PROBLEM: pokaż restauracje, które mają 9 opinii.
SOLUTION: show me restaurants with a 9 reviews .
PROBLEM: powiedz mi, jakie są restauracje " american ".
SOLUTION: tell me the " american " restaurants .
PROBLEM: wyświetl wszystkie restauracje z oceną 2 lub wyższą.
SOLUTION:
|
display any restaurants with overall rating of 2 or above .
|
task263_spl_translation_pl_en
|
NIv2
|
fs_opt
| 8
|
train
|
Teacher:The provided file includes inquiries about restaurants in Polish, and we ask you to translate those to English language. Please bear in mind the following guidelines while doing the translation: 1) We are looking for the most naturally written and formal form of each sentence in your language. We are *NOT* looking for colloquial forms of the sentence. We are looking for formal form which is how you would type your queries in a text-based virtual assistant. 2) The words between quotation marks *SHOULD NOT* be translated. We expect you to keep those values intact and include the quotation marks around them as well. 3) The fully capitalized words like DATE_0, or DURATION_0 *SHOULD NOT* be translated. Please keep them as they are in the translations. 4) Please do not localize measurement units like miles to kilometers during your translation. miles should be translated to its equivalent in your language. 6) Note the input is all lowercased except for fully capitalized special placeholders (e.g. NUMBER, DATE, TIME). Please do the same in your translations.
Teacher: Now, understand the problem? Solve this instance: szukaj " mexican " restauracji
Student:
|
search for " mexican " restaurants
|
task263_spl_translation_pl_en
|
NIv2
|
zs_opt
| 6
|
test
|
instruction:
The provided file includes inquiries about restaurants in Polish, and we ask you to translate those to English language. Please bear in mind the following guidelines while doing the translation: 1) We are looking for the most naturally written and formal form of each sentence in your language. We are *NOT* looking for colloquial forms of the sentence. We are looking for formal form which is how you would type your queries in a text-based virtual assistant. 2) The words between quotation marks *SHOULD NOT* be translated. We expect you to keep those values intact and include the quotation marks around them as well. 3) The fully capitalized words like DATE_0, or DURATION_0 *SHOULD NOT* be translated. Please keep them as they are in the translations. 4) Please do not localize measurement units like miles to kilometers during your translation. miles should be translated to its equivalent in your language. 6) Note the input is all lowercased except for fully capitalized special placeholders (e.g. NUMBER, DATE, TIME). Please do the same in your translations.
question:
znajdź wszystkie restauracje z oceną około 10.
answer:
find all of the restaurants that have a rating about 10 .
question:
szukaj " mexican " restauracji
answer:
search for " mexican " restaurants
question:
znajdź " italian " restauracje .
answer:
|
find " italian " restaurants .
|
task263_spl_translation_pl_en
|
NIv2
|
fs_opt
| 9
|
validation
|
You will be given a definition of a task first, then some input of the task.
A text is given in Hindi. Translate it from the Hindi language to the Malayalam language. The translation must not omit or add information to the original sentence.
രാജ്യത്ത് ഏറ്റവും പിന്നാക്കം നില്ക്കുന്ന 100 ജില്ലകളില് ശ്രദ്ധയൂന്നാനും, അതു വഴി വിവിധ വികസന മാനദണ്ഡങ്ങളില് ദേശീയ ശരാരിയിലേക്ക് അവയെ ഉയര്ത്തിക്കൊണ്ടുവരാനും പ്രധാനമന്ത്രി ഉദ്യോഗസ്ഥരോടാവശ്യപ്പെട്ടു.
Output:
|
उन्होंने अधिकारियों को भारत के 100 सबसे पिछड़े जिलों पर ध्यान केंद्रित करने के लिए कहा ताकि उन्हें विभिन्न विकास मानकों पर राष्ट्रीय औसत स्तर के करीब लाया जा सके।
|
task1018_pib_translation_malayalam_hindi
|
NIv2
|
zs_opt
| 1
|
train
|
A text is given in Hindi. Translate it from the Hindi language to the Malayalam language. The translation must not omit or add information to the original sentence.
പാവപ്പെട്ടവരുടെയും എളുപ്പത്തില് ചൂഷണം ചെയ്യപ്പെടാവുന്നവരുടേയും സാമ്പത്തിക സുരക്ഷിതത്വം ഉറപ്പു വരുത്താന് ഗവണ്മെന്റ് സ്വീകരിച്ച നടപടികള് പ്രധാനമന്ത്രി വിശദീകരിച്ചു.
|
प्रधानमंत्री ने गरीबों और वंचितों को वित्तीय सुरक्षा सुनिश्चित करने के लिए सरकार द्वारा उठाए गए विभिन्न उपायों के बारे में भी बताया।
|
task1018_pib_translation_malayalam_hindi
|
NIv2
|
zs_opt
| 0
|
train
|
Detailed Instructions: A text is given in Hindi. Translate it from the Hindi language to the Malayalam language. The translation must not omit or add information to the original sentence.
Q: വിദേശ വിപണികളില് ഉയര്ന്ന സാങ്കേതികവിദ്യാ ഉല്പ്പന്നങ്ങള് എത്തിക്കുന്നതിന് രൂപകല്പ്പനയിലും വികാസത്തിലും ഉല്പ്പാദനത്തിലും നാം സഹകരണം വിശാലമാക്കുകയും ബഹിരാകാശ സാങ്കേതികവിദ്യ, വ്യോമയാനം, പുതിയ ഉപകരണങ്ങള്, കൃഷി, വിവര-ആശയവിനിമയ സാങ്കേതികവിദ്യകള്, മരുന്ന്, ഔഷധങ്ങള്, റോബോട്ടിക്സ്, നാനോ സാങ്കേതികവിദ്യ, സൂപ്പര് കമ്പ്യൂട്ടിംഗ് സാങ്കേതികവിദ്യകള്, കൃത്രിമ ബൗദ്ധികത, പദാര്ത്ഥ ശാസാത്രം തുടങ്ങിയ മേഖലകളില് ശാസ്ത്രീയ സഹകരണ ശക്തിപ്പെടുത്തുകയും ചെയ്യും.
A:
|
हम अंतरिक्ष प्रौद्योगिकी, विमानन, नए पदार्थ, कृषि, सूचना एवं संचार प्रौद्योगिकी, दवा, फार्मास्युटिकल्स, रोबोटिक्स, नैनोटेक्नोलॉजी, सुपरकम्प्यूटिंग तकनीकी, कृत्रिम बौद्धिकता एवं भौतिक विज्ञान जैसे क्षेत्रों में वैज्ञानिक सहयोग को मजबूती देने और विदेशी बाजारों में उच्च प्रौद्योगिकी वाले उत्पादों को उतारने के लिए डिजाइन, विकास एवं विनिर्माण में सहयोग का दायरा बढ़ाएंगे।
|
task1018_pib_translation_malayalam_hindi
|
NIv2
|
zs_opt
| 9
|
train
|
Detailed Instructions: A text is given in Hindi. Translate it from the Hindi language to the Malayalam language. The translation must not omit or add information to the original sentence.
Q: ഇത്രയ്ക്ക് ഉന്നത ഗുണനിലവാരമുള്ള തൊഴില് മേഖലയിലെ ആളുകളുടെ കാര്യത്തിലിങ്ങനെയാണ് നടപടികളെന്നത് വേവലാതിപ്പെടുത്തുന്നതല്ലേ എന്ന് എന്റെ സുഹൃത്തുക്കള് പറയൂ.
A:
|
इतने igh ualiy Proessionals के लिये मेरे साथियों बताइए ये चिंता का विषय है कि नहीं है।
|
task1018_pib_translation_malayalam_hindi
|
NIv2
|
zs_opt
| 9
|
train
|
Teacher: A text is given in Hindi. Translate it from the Hindi language to the Malayalam language. The translation must not omit or add information to the original sentence.
Teacher: Now, understand the problem? If you are still confused, see the following example:
മാനവികത ഒരുമിച്ചു ചേരുമ്പോള് അദ്ഭുതങ്ങള് സംഭവിക്കുന്നു എന്നാണിത് കാണിക്കുന്നത്
Solution: इससे पता चलता है कि मानवता एक साथ होती है
Reason: Correct translation for given sentence. Input sentence means 'This shows that humanity happens together' which is the same as the output sentence.
Now, solve this instance: ജാര്ഖണ്ടിനായി ദൂരദര്ശന്റെ പ്രത്യേക ചാനല് പ്രഖ്യാപിച്ചു.
Student:
|
झारखंड के लिए एक अलग 247 डीडी चैनल की घोषणा की गई।
|
task1018_pib_translation_malayalam_hindi
|
NIv2
|
fs_opt
| 2
|
train
|
Instructions: A text is given in Hindi. Translate it from the Hindi language to the Malayalam language. The translation must not omit or add information to the original sentence.
Input: സര്ദാര് വല്ലഭായ് പട്ടിേലിന്റെ ജന്മനാടായ ഗുജറാത്തിലെ നര്മദ ജില്ലയിലുള്ള കെവാദിയയില് അദ്ദേഹത്തിന്റെ ജന്മവാര്ഷിക നാളിലാണ് 182 മീറ്റര് ഉയരമുള്ള പ്രതിമ രാഷ്ട്രത്തിനു സമര്പ്പിക്കപ്പെട്ടത്.
Output:
|
लौह पुरुष सरदार वल्लभभाई पटेल की जयंती पर 182 मीटर की उनकी प्रतिमा आज गुजरात के नर्मदा जिले के केवड़िया में राष्ट्र को समर्पित की गई।
|
task1018_pib_translation_malayalam_hindi
|
NIv2
|
zs_opt
| 3
|
train
|
Teacher:A text is given in Hindi. Translate it from the Hindi language to the Malayalam language. The translation must not omit or add information to the original sentence.
Teacher: Now, understand the problem? Solve this instance: രാജ്യത്തിനെതിരെ പ്രവര്ത്തിക്കുന്ന ശക്തികള്ക്കെതിരെ കര്ശന നടപടികള് കൈക്കൊള്ളുമെന്ന് അദ്ദേഹം ജനങ്ങള്ക്ക് ഉറപ്പ് നല്കി.
Student:
|
उन्होंने लोगों को आश्वस्त करते हुए कहा कि राष्ट्र के हितों के विरूद्ध काम करने वाले लोगों के खिलाफ कड़ी कार्रवाई की जाएगी।
|
task1018_pib_translation_malayalam_hindi
|
NIv2
|
zs_opt
| 6
|
train
|
A text is given in Hindi. Translate it from the Hindi language to the Malayalam language. The translation must not omit or add information to the original sentence.
Example input: മാനവികത ഒരുമിച്ചു ചേരുമ്പോള് അദ്ഭുതങ്ങള് സംഭവിക്കുന്നു എന്നാണിത് കാണിക്കുന്നത്
Example output: इससे पता चलता है कि मानवता एक साथ होती है
Example explanation: Correct translation for given sentence. Input sentence means 'This shows that humanity happens together' which is the same as the output sentence.
Q: ഞാന് ഇന്ത്യയും അഫ്ഗാനിസ്ഥാനും തമ്മില് നട ടെസ്റ്റ് മാച്ചിനെക്കുറിച്ചാണു പറയുതെ് നിങ്ങള്ക്ക് വ്യക്തമായി മനസ്സിലായിക്കാണും.
A:
|
आप लोग भली-भांति समझ गए होंगे कि मैं भारत और अफगानिस्तान के टेस्ट मैच की बात कर रहा हू।
|
task1018_pib_translation_malayalam_hindi
|
NIv2
|
fs_opt
| 3
|
train
|
Q: A text is given in Hindi. Translate it from the Hindi language to the Malayalam language. The translation must not omit or add information to the original sentence.
പ്രധാനമന്ത്രി, അടുത്തിടെ നടന്ന പൊതു തെരഞ്ഞെടുപ്പിനെ ഏറ്റവും വലിയ ജനാധിപത്യ പ്രവര്ത്തനമെന്നു വിശേഷിപ്പിക്കുകയും ഇനിയുള്ള നാളുകള് എല്ലാവരും ചേര്ന്ന് ഇന്ത്യയുടെ വികസനത്തിനായി പ്രവര്ത്തിക്കേണ്ട കാലമാണെന്ന് ഓര്മിപ്പിക്കുകയും ചെയ്തു.
A:
|
प्रधानमंत्री श्री मोदी ने हाल के आम चुनाव को दुनिया की सबसे बड़ी लोकतांत्रिक प्रक्रिया बताते हुए कहा कि अब समय आ गया है कि सभी लोग मिलकर भारत के विकास के लिए कार्य करें।
|
task1018_pib_translation_malayalam_hindi
|
NIv2
|
zs_opt
| 7
|
test
|
A text is given in Hindi. Translate it from the Hindi language to the Malayalam language. The translation must not omit or add information to the original sentence.
One example is below.
Q: മാനവികത ഒരുമിച്ചു ചേരുമ്പോള് അദ്ഭുതങ്ങള് സംഭവിക്കുന്നു എന്നാണിത് കാണിക്കുന്നത്
A: इससे पता चलता है कि मानवता एक साथ होती है
Rationale: Correct translation for given sentence. Input sentence means 'This shows that humanity happens together' which is the same as the output sentence.
Q: ഇന്ത്യ ലോക സമ്പദ്ഘടനയെ താഴേക്ക് വലിക്കുന്നുവെന്ന ആശങ്ക അവര്ക്കുണ്ടായിരുന്നു. എന്നാല് ഇന്ന് ഇന്ത്യ ഒരു ബഹു-ട്രില്യന് ഡോളര് നിക്ഷേപ കേന്ദ്രമായി മാറിയതോടെ അവരുടെ സ്വരവും മാറിയിരിക്കുന്നു.
A:
|
लेकिन आज दुनिया कह रही है कि भारत multi trillion dollar के investment का destination बन गया है। वहीं से आवाज बदल गई है।
|
task1018_pib_translation_malayalam_hindi
|
NIv2
|
fs_opt
| 9
|
validation
|
Definition: You are given a sentence in English language and we ask you to translate those to the Gujarati language. Consider following guidelines while doing the translation: 1) Note the input can be lowercase or uppercase. Please do the same in your translations. 2) Keep the short forms like '%s', 'CMD' intact as the literal meaning does not make sense.
Input: The UPS is below the critical level and this computer is about to shutdown.
Output:
|
UPS એ જટિલ સ્તરની નીચે છે અને આ કમ્પ્યૂટર બંધ થવા જઈ રહ્યું છે.
|
task1350_opus100_translation_en_gu
|
NIv2
|
zs_opt
| 2
|
train
|
You are given a sentence in English language and we ask you to translate those to the Gujarati language. Consider following guidelines while doing the translation: 1) Note the input can be lowercase or uppercase. Please do the same in your translations. 2) Keep the short forms like '%s', 'CMD' intact as the literal meaning does not make sense.
Enter the server which hosts the room, or leave it empty if the room is on the current accountapos;s server
|
સર્વર ને દાખલ કરો કે જે રુમનાં યજમાનો છે, અથવા તેને ખાલી છોડી દો જો રુમ એ હાલનાં ખાતાનાં સર્વર પર હોય તો
|
task1350_opus100_translation_en_gu
|
NIv2
|
zs_opt
| 0
|
train
|
You are given a sentence in English language and we ask you to translate those to the Gujarati language. Consider following guidelines while doing the translation: 1) Note the input can be lowercase or uppercase. Please do the same in your translations. 2) Keep the short forms like '%s', 'CMD' intact as the literal meaning does not make sense.
If you do not send a cancelation notice, the other participants may not know the memo has been deleted.
|
જો તમે નકારાત્મક સૂચન મોકલો નહિં, તો બીજા સ્પર્ધકોને ખબર નહિં પડે કે મેમો કાઢી નંખાયેલ છે.
|
task1350_opus100_translation_en_gu
|
NIv2
|
zs_opt
| 0
|
train
|
You are given a sentence in English language and we ask you to translate those to the Gujarati language. Consider following guidelines while doing the translation: 1) Note the input can be lowercase or uppercase. Please do the same in your translations. 2) Keep the short forms like '%s', 'CMD' intact as the literal meaning does not make sense.
Input: Consider Input: Forgetting your passwords will clear all remembered passwords. You will be reprompted next time they are needed.
Output: તમારા પાસવર્ડો ભૂલી જવાનું યાદ રાખેલ બધા પાસવર્ડો સાફ કરી નાંખશે. તમે આગળના સમયે જ્યારે જરૂરી હોય ત્યારે ફરીથી પૂછવામાં આવશે.
Input: Consider Input: %s: gnome-session not found for a failsafe GNOME session; trying xterm
Output: %s: gnome-session એ failsafe GNOME સત્ર માટે મળ્યો નથી; xterm નો પ્રયાસ કરી રહ્યા છીએ
Input: Consider Input: You are not allowed to access the device. Contact your system administrator.
|
Output: તમને ઉપકરણ ને દાખલ કરવા માટે પરવાનગી આપેલ નથી. તમારા સિસ્ટમ વહીવટકર્તાને સંપર્ક કરો.
|
task1350_opus100_translation_en_gu
|
NIv2
|
fs_opt
| 2
|
train
|
TASK DEFINITION: You are given a sentence in English language and we ask you to translate those to the Gujarati language. Consider following guidelines while doing the translation: 1) Note the input can be lowercase or uppercase. Please do the same in your translations. 2) Keep the short forms like '%s', 'CMD' intact as the literal meaning does not make sense.
PROBLEM: Convert to Indexed using default settings (Do it manually to tune the result)
SOLUTION: મૂળભૂત સુયોજનોની મદદથી અનુક્રમિતમાં ફેરવો (પરિણામ જાતે સુયોજિત કરો)
PROBLEM: The password you use to log in to your computer no longer matches that of your login keyring.
SOLUTION: પાસવર્ડ કે જે તમે તમારા કમ્પ્યૂટર માટે વાપરો છો તે તમારાં પ્રવેશ કીરીંગ સાથે લાંબા સમય સુધી બંધબેસશે નહિં.
PROBLEM: Running graphical applications as a privileged user should be avoided for security reasons.
SOLUTION:
|
અધિકાર થયેલ વપરાશકર્તા તરીકે ચાલતા ગ્રાફીકલ કાર્યક્રમો સુરક્ષા કારણો માટે અવગણવા જોઇએ.
|
task1350_opus100_translation_en_gu
|
NIv2
|
fs_opt
| 8
|
train
|
TASK DEFINITION: You are given a sentence in English language and we ask you to translate those to the Gujarati language. Consider following guidelines while doing the translation: 1) Note the input can be lowercase or uppercase. Please do the same in your translations. 2) Keep the short forms like '%s', 'CMD' intact as the literal meaning does not make sense.
PROBLEM: A read receipt notification has been requested for "{1}". Send the receipt notification to {0}?
SOLUTION: વાંચન રસીદ સૂચને "{1}" માટે અરજી કરી છે. શું {0} ને રસીદ સૂચન મોકલવું છે?
PROBLEM: Empathy is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version.
SOLUTION: Empathy is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version.
PROBLEM: Configure GDM (this login manager). This will require the root password.
SOLUTION:
|
જીડીએમ રુપરેખાંકિત કરો (આ પ્રવેશ પ્રક્રિયા સંચાલક). આ માટે પાસવર્ડની જરુર પડશે.
|
task1350_opus100_translation_en_gu
|
NIv2
|
fs_opt
| 8
|
train
|
Teacher:You are given a sentence in English language and we ask you to translate those to the Gujarati language. Consider following guidelines while doing the translation: 1) Note the input can be lowercase or uppercase. Please do the same in your translations. 2) Keep the short forms like '%s', 'CMD' intact as the literal meaning does not make sense.
Teacher: Now, understand the problem? Solve this instance: Failed to start the X server (your graphical interface). It is likely that it is not set up correctly. You will need to log in on a console and reconfigure the X server. Then restart GDM.
Student:
|
X સર્વર (તમારું ગ્રાફિકવાળું ઈન્ટરફેસ) શરૂ કરવામાં નિષ્ફળ. એવું લાગે છે કે તે યોગ્ય રીતે સુયોજિત થયેલ નથી. તમારે કન્સોલમાં પ્રવેશ કરવાની જરૂર રહેશે અને X સર્વર પુનઃરૂપરેખાંકિત કરવું પડશે. પછી GDM પુનઃશરૂ કરો.
|
task1350_opus100_translation_en_gu
|
NIv2
|
zs_opt
| 6
|
train
|
You are given a sentence in English language and we ask you to translate those to the Gujarati language. Consider following guidelines while doing the translation: 1) Note the input can be lowercase or uppercase. Please do the same in your translations. 2) Keep the short forms like '%s', 'CMD' intact as the literal meaning does not make sense.
[EX Q]: Authentication is required to modify the lit LED for a Wacom tablet
[EX A]: Wacom ટૅબલેટ માટે લીટ LED ને બદલવા માટે સત્તાધિકરણની જરૂર છે
[EX Q]: Odd character '%s', expected a '>' character to end the empty-element tag '%s'
[EX A]: અસંગત અક્ષર '%s', વસ્તુ ટેગ '%s' નાં ખાલી ઘટક ને સમાપ્ત કરવા '>' અક્ષર ની આશા છે
[EX Q]: Waiting for program to finish. Interrupting program may cause you to lose work.
[EX A]:
|
સમાપ્ત કરવા પ્રક્રિયા માટે રાહ જોઇ રહ્યા છે. અટકાવેલી પ્રક્રિયા એ તમે તમારા કામ ને ગુમાવવાનું કારણ બની શકે છે.
|
task1350_opus100_translation_en_gu
|
NIv2
|
fs_opt
| 6
|
train
|
Teacher: You are given a sentence in English language and we ask you to translate those to the Gujarati language. Consider following guidelines while doing the translation: 1) Note the input can be lowercase or uppercase. Please do the same in your translations. 2) Keep the short forms like '%s', 'CMD' intact as the literal meaning does not make sense.
Teacher: Now, understand the problem? If you are still confused, see the following example:
Select your IMAP Header Preferences. The more headers you have the more time it will take to download.
Solution: તમારી IMAP હેડર પસંદગીઓ પસંદ કરો. જેટલી વધી હેડરો એટલો વધુ સમય તે ડાઉનલોડ કરવામાં લેશે.
Reason: The translation is accurate and preserves 'IMAP' as it does not have meaning in Gujarati.
Now, solve this instance: You don't have the right permissions to read files from folder "%s"
Student:
|
તમારી પાસે "%s" ફાઇલો વાંચવાની સત્તા નથી
|
task1350_opus100_translation_en_gu
|
NIv2
|
fs_opt
| 2
|
test
|
You are given a sentence in English language and we ask you to translate those to the Gujarati language. Consider following guidelines while doing the translation: 1) Note the input can be lowercase or uppercase. Please do the same in your translations. 2) Keep the short forms like '%s', 'CMD' intact as the literal meaning does not make sense.
One example is below.
Q: Select your IMAP Header Preferences. The more headers you have the more time it will take to download.
A: તમારી IMAP હેડર પસંદગીઓ પસંદ કરો. જેટલી વધી હેડરો એટલો વધુ સમય તે ડાઉનલોડ કરવામાં લેશે.
Rationale: The translation is accurate and preserves 'IMAP' as it does not have meaning in Gujarati.
Q: Pair cdr type must be a primitive type: string, int, float or bool
A:
|
cdr જોડીનો પ્રકાર એક મૂળ પ્રકાર હોવો જોઇએઃ string, int, float અથવા bool
|
task1350_opus100_translation_en_gu
|
NIv2
|
fs_opt
| 9
|
validation
|
Classify the given tweet into the three categories: (1) 'Hate Speech', (2) 'Offensive' and (3) 'Neither'. 'Hate Speech' is kind of a threating statement or sometimes include call for violence while 'offensive' statement just offensds someone. 'Neither' is when it doesn't fall into Hate Speech or Offensive category.
Q: @Walmart fuck your store bitch
A:
|
Hate Speech
|
task904_hate_speech_offensive_classification
|
NIv2
|
zs_opt
| 4
|
train
|
Classify the given tweet into the three categories: (1) 'Hate Speech', (2) 'Offensive' and (3) 'Neither'. 'Hate Speech' is kind of a threating statement or sometimes include call for violence while 'offensive' statement just offensds someone. 'Neither' is when it doesn't fall into Hate Speech or Offensive category.
[Q]: “@HumbleTeej: @elisabethepps where am I on this rating scale???” Depends. You still wearing them colored contacts or naw?
[A]: Neither
[Q]: Bigots demand recount in #Idaho #gay rights ordinance referendum http://t.co/TorYEXxMwi #tcot #teaparty #teabaggers #lgbt
[A]: Neither
[Q]: #Yankees I went to Golds with Cruz in 2010 when he was a Sound. No upper /lower body strength.. Hmm now 40 HR hitter. Won't say the S word.
[A]:
|
Neither
|
task904_hate_speech_offensive_classification
|
NIv2
|
fs_opt
| 5
|
train
|
Given the task definition and input, reply with output. Classify the given tweet into the three categories: (1) 'Hate Speech', (2) 'Offensive' and (3) 'Neither'. 'Hate Speech' is kind of a threating statement or sometimes include call for violence while 'offensive' statement just offensds someone. 'Neither' is when it doesn't fall into Hate Speech or Offensive category.
Nike DC is trash.
|
Neither
|
task904_hate_speech_offensive_classification
|
NIv2
|
zs_opt
| 5
|
train
|
Classify the given tweet into the three categories: (1) 'Hate Speech', (2) 'Offensive' and (3) 'Neither'. 'Hate Speech' is kind of a threating statement or sometimes include call for violence while 'offensive' statement just offensds someone. 'Neither' is when it doesn't fall into Hate Speech or Offensive category.
Q: @B_McC10ud that's meant toward my dads gf shes a bitch dude
A:
|
Offensive
|
task904_hate_speech_offensive_classification
|
NIv2
|
zs_opt
| 4
|
train
|
Classify the given tweet into the three categories: (1) 'Hate Speech', (2) 'Offensive' and (3) 'Neither'. 'Hate Speech' is kind of a threating statement or sometimes include call for violence while 'offensive' statement just offensds someone. 'Neither' is when it doesn't fall into Hate Speech or Offensive category.
Let me give you an example: "@MarkRoundtreeJr: LMFAOOOO I HATE BLACK PEOPLE https://t.co/RNvD2nLCDR" This is why there's black people and niggers
The answer to this example can be: Hate Speech
Here is why: The writer mentions emotions like hating so it is clearly a hate speech.
OK. solve this:
Four hoûrs to back up this iPhone haha... Jokes 😒
Answer:
|
Neither
|
task904_hate_speech_offensive_classification
|
NIv2
|
fs_opt
| 8
|
train
|
Detailed Instructions: Classify the given tweet into the three categories: (1) 'Hate Speech', (2) 'Offensive' and (3) 'Neither'. 'Hate Speech' is kind of a threating statement or sometimes include call for violence while 'offensive' statement just offensds someone. 'Neither' is when it doesn't fall into Hate Speech or Offensive category.
Q: Yost on Portsmouth "this is the biggest white trash county in the nation" #YostKnowsMost @iDOWNTOWN
A:
|
Hate Speech
|
task904_hate_speech_offensive_classification
|
NIv2
|
zs_opt
| 9
|
train
|
Classify the given tweet into the three categories: (1) 'Hate Speech', (2) 'Offensive' and (3) 'Neither'. 'Hate Speech' is kind of a threating statement or sometimes include call for violence while 'offensive' statement just offensds someone. 'Neither' is when it doesn't fall into Hate Speech or Offensive category.
[EX Q]: “@NayMinaj_xo: @SkinnyBee__ LMAO my bitch was listening to gospel music w. liquor in her hand weaaakkk”lmfaooooooooo I was crying
[EX A]: Offensive
[EX Q]: I was informed about a bird (pheonix) today that I could not find a physical image of.All paintings.Mythological or extinct?
[EX A]: Neither
[EX Q]: I said middle school days as in the ghetto way people... — and what I am nor here to impress you what are you go... http://t.co/qYpvIKcRfY
[EX A]:
|
Neither
|
task904_hate_speech_offensive_classification
|
NIv2
|
fs_opt
| 6
|
train
|
You will be given a definition of a task first, then an example. Follow the example to solve a new instance of the task.
Classify the given tweet into the three categories: (1) 'Hate Speech', (2) 'Offensive' and (3) 'Neither'. 'Hate Speech' is kind of a threating statement or sometimes include call for violence while 'offensive' statement just offensds someone. 'Neither' is when it doesn't fall into Hate Speech or Offensive category.
"@MarkRoundtreeJr: LMFAOOOO I HATE BLACK PEOPLE https://t.co/RNvD2nLCDR" This is why there's black people and niggers
Solution: Hate Speech
Why? The writer mentions emotions like hating so it is clearly a hate speech.
New input: RT @MichaelGT03: Happy birthday freshman faggots @cooopster98 @Chase123456718
Solution:
|
Hate Speech
|
task904_hate_speech_offensive_classification
|
NIv2
|
fs_opt
| 0
|
train
|
Classify the given tweet into the three categories: (1) 'Hate Speech', (2) 'Offensive' and (3) 'Neither'. 'Hate Speech' is kind of a threating statement or sometimes include call for violence while 'offensive' statement just offensds someone. 'Neither' is when it doesn't fall into Hate Speech or Offensive category.
@KuntaKayne I have never seen such ignorant, illiterate pieces of shit in my life. Bring in Natl Guard & disperse this trash. @bassem_masri
|
Hate Speech
|
task904_hate_speech_offensive_classification
|
NIv2
|
zs_opt
| 0
|
test
|
You will be given a definition of a task first, then some input of the task.
Classify the given tweet into the three categories: (1) 'Hate Speech', (2) 'Offensive' and (3) 'Neither'. 'Hate Speech' is kind of a threating statement or sometimes include call for violence while 'offensive' statement just offensds someone. 'Neither' is when it doesn't fall into Hate Speech or Offensive category.
“@_BeautifulKeezy: these hoes be playing w/ me like I won't send baby Cain at they niggah !!”yesss messy.
Output:
|
Offensive
|
task904_hate_speech_offensive_classification
|
NIv2
|
zs_opt
| 1
|
validation
|
In this task, you are given a premise and hypothesis. The task is to classify them into three categories: 'positive' if the hypothesis supports the premise, 'negated' if it opposes the premise, and 'neutral' if it neither supports nor opposes it.
Premise : 'The five guests that answered die.','Hypothesis : There are exactly five guests that answered.'
|
positive
|
task1516_imppres_naturallanguageinference
|
NIv2
|
zs_opt
| 0
|
train
|
Teacher:In this task, you are given a premise and hypothesis. The task is to classify them into three categories: 'positive' if the hypothesis supports the premise, 'negated' if it opposes the premise, and 'neutral' if it neither supports nor opposes it.
Teacher: Now, understand the problem? Solve this instance: Premise : 'All three actors that could chat might have commissioned Gregory to exist.','Hypothesis : There aren't exactly three actors that could chat.'
Student:
|
negated
|
task1516_imppres_naturallanguageinference
|
NIv2
|
zs_opt
| 6
|
train
|
Part 1. Definition
In this task, you are given a premise and hypothesis. The task is to classify them into three categories: 'positive' if the hypothesis supports the premise, 'negated' if it opposes the premise, and 'neutral' if it neither supports nor opposes it.
Part 2. Example
'Premise : All ten guys that proved to boast were divorcing.','Hypothesis : There are exactly ten guys that proved to boast.'
Answer: positive
Explanation: The output of the given hypothesis and hremise is 'Positive' as the hypothesis supports the premise in a 'Positive' way. In the premise it is mentioned ten guys and even in the hypothesis, ten guys are mentioned
Part 3. Exercise
Premise : 'If all seven dancers who joked around do return to Clyde, it's okay.','Hypothesis : There are more than seven dancers who joked around.'
Answer:
|
negated
|
task1516_imppres_naturallanguageinference
|
NIv2
|
fs_opt
| 7
|
train
|
In this task, you are given a premise and hypothesis. The task is to classify them into three categories: 'positive' if the hypothesis supports the premise, 'negated' if it opposes the premise, and 'neutral' if it neither supports nor opposes it.
[EX Q]: Premise : 'The five dishes that astounded Kenneth might chip.','Hypothesis : There are exactly five vases that astounded Kenneth.'
[EX A]: neutral
[EX Q]: Premise : 'All ten ladies that discover those doors might judge Nina to mess up a rug.','Hypothesis : There aren't exactly ten ladies that discover those doors.'
[EX A]: negated
[EX Q]: Premise : 'The ten women that talk didn't implore Pamela to bore Kenneth.','Hypothesis : There are exactly ten committees that talk.'
[EX A]:
|
neutral
|
task1516_imppres_naturallanguageinference
|
NIv2
|
fs_opt
| 6
|
train
|
Given the task definition, example input & output, solve the new input case.
In this task, you are given a premise and hypothesis. The task is to classify them into three categories: 'positive' if the hypothesis supports the premise, 'negated' if it opposes the premise, and 'neutral' if it neither supports nor opposes it.
Example: 'Premise : All ten guys that proved to boast were divorcing.','Hypothesis : There are exactly ten guys that proved to boast.'
Output: positive
The output of the given hypothesis and hremise is 'Positive' as the hypothesis supports the premise in a 'Positive' way. In the premise it is mentioned ten guys and even in the hypothesis, ten guys are mentioned
New input case for you: Premise : 'If all ten ladies that discover those doors judge Nina to mess up a rug, it's okay.','Hypothesis : There aren't exactly ten ladies that discover those doors.'
Output:
|
negated
|
task1516_imppres_naturallanguageinference
|
NIv2
|
fs_opt
| 1
|
train
|
TASK DEFINITION: In this task, you are given a premise and hypothesis. The task is to classify them into three categories: 'positive' if the hypothesis supports the premise, 'negated' if it opposes the premise, and 'neutral' if it neither supports nor opposes it.
PROBLEM: Premise : 'If all seven waiters that were scaring Suzanne have won, it's okay.','Hypothesis : There are exactly seven waiters that were scaring Suzanne.'
SOLUTION: positive
PROBLEM: Premise : 'The seven women who shouldn't research these grocery stores might have arrived at these closets.','Hypothesis : There are exactly eight women who shouldn't research these grocery stores.'
SOLUTION: negated
PROBLEM: Premise : 'The six people who had failed to litter might have kissed customers.','Hypothesis : There are exactly seven people who had failed to litter.'
SOLUTION:
|
negated
|
task1516_imppres_naturallanguageinference
|
NIv2
|
fs_opt
| 8
|
train
|
In this task, you are given a premise and hypothesis. The task is to classify them into three categories: 'positive' if the hypothesis supports the premise, 'negated' if it opposes the premise, and 'neutral' if it neither supports nor opposes it.
One example is below.
Q: 'Premise : All ten guys that proved to boast were divorcing.','Hypothesis : There are exactly ten guys that proved to boast.'
A: positive
Rationale: The output of the given hypothesis and hremise is 'Positive' as the hypothesis supports the premise in a 'Positive' way. In the premise it is mentioned ten guys and even in the hypothesis, ten guys are mentioned
Q: Premise : 'If all seven dancers who joked around do return to Clyde, it's okay.','Hypothesis : There are exactly seven dancers who joked around.'
A:
|
positive
|
task1516_imppres_naturallanguageinference
|
NIv2
|
fs_opt
| 9
|
train
|
In this task, you are given a premise and hypothesis. The task is to classify them into three categories: 'positive' if the hypothesis supports the premise, 'negated' if it opposes the premise, and 'neutral' if it neither supports nor opposes it.
Example: 'Premise : All ten guys that proved to boast were divorcing.','Hypothesis : There are exactly ten guys that proved to boast.'
Example solution: positive
Example explanation: The output of the given hypothesis and hremise is 'Positive' as the hypothesis supports the premise in a 'Positive' way. In the premise it is mentioned ten guys and even in the hypothesis, ten guys are mentioned
Problem: Premise : 'All three libraries that haven't needed to listen to George did forget the waiter.','Hypothesis : There are exactly three libraries that haven't needed to listen to George.'
|
Solution: positive
|
task1516_imppres_naturallanguageinference
|
NIv2
|
fs_opt
| 5
|
train
|
Teacher:In this task, you are given a premise and hypothesis. The task is to classify them into three categories: 'positive' if the hypothesis supports the premise, 'negated' if it opposes the premise, and 'neutral' if it neither supports nor opposes it.
Teacher: Now, understand the problem? Solve this instance: Premise : 'All nine teenagers that haven't spurred many nieces of Liam to explain everything have observed Jacqueline.','Hypothesis : There are exactly ten teenagers that haven't spurred many nieces of Liam to explain everything.'
Student:
|
negated
|
task1516_imppres_naturallanguageinference
|
NIv2
|
zs_opt
| 6
|
test
|
In this task, you are given a premise and hypothesis. The task is to classify them into three categories: 'positive' if the hypothesis supports the premise, 'negated' if it opposes the premise, and 'neutral' if it neither supports nor opposes it.
Q: Premise : 'If all two libraries that considered Scott to hate this teenager were attempting to disturb Todd, it's okay.','Hypothesis : There are exactly two libraries that considered Scott to hate this teenager.'
A:
|
positive
|
task1516_imppres_naturallanguageinference
|
NIv2
|
zs_opt
| 4
|
validation
|
Detailed Instructions: You are given a sentence in Arabic. Your job is to translate the Arabic sentence into Spanish.
Q: وعلى الأفرع تنمو مواد المكتبة
A:
|
Y en las ramas crece el material de la biblioteca.
|
task1232_ted_translation_ar_es
|
NIv2
|
zs_opt
| 9
|
train
|
You will be given a definition of a task first, then an example. Follow the example to solve a new instance of the task.
You are given a sentence in Arabic. Your job is to translate the Arabic sentence into Spanish.
فالفرقة الأولى من أجهزة الحاسب سيتم تشغيلها هذا الصيف و نحن نساعد د. زنفلر في صنع استراتيجيات حول كيفية امكانية ربط الفصل مع البيت ليمتد التعليم الى بعد اليوم المدرسي.
Solution: A fines del verano estamos instalando el primer lote de computadoras y ayudando al Dr. Zullinger a desarrollar estrategias para poder conectar el aula y el hogar y extender el aprendizaje más allá del día escolar.
Why? The Arabic sentence is correctly translated into Spanish, because the meaning is preserved.
New input: مشروعنا الرايخستاج ، الذي لديه أجندة معروفة. أنا متأكد ، كمكان عام حيث نفكر فيه ، الوسيلة ، من خلال عملية الدعوة إليه ، وإعادة تفسير العلاقة بين المجتمع والسياسيين ، المساحة العامة. ربما إنها أجندة خفية ، بيان الطاقة الرسمي (المنفستو) — الشئ الذي ربما يكون خاليا ، خالي تماما من الوقود كما نعرفه.
Solution:
|
Nuestro proyecto Reichstag, que tiene una agenda muy familiar, estoy seguro, como un espacio público donde buscamos a través de procesos de abogacía, reinterpretar la relación entre la sociedad y los políticos, el espacio público y quizá su agenda oculta, un manifiesto por la energía — algo que pueda ser libre, completamente libre de combustible como lo conocemos.
|
task1232_ted_translation_ar_es
|
NIv2
|
fs_opt
| 0
|
train
|
Instructions: You are given a sentence in Arabic. Your job is to translate the Arabic sentence into Spanish.
Input: بالتأكيد ، هذا الكائن عباره عن مزرعه محليه ، للأسماك والنباتات.
Output:
|
Realmente, este objeto es una granja doméstica, para peces y hortalizas.
|
task1232_ted_translation_ar_es
|
NIv2
|
zs_opt
| 3
|
train
|
You will be given a definition of a task first, then some input of the task.
You are given a sentence in Arabic. Your job is to translate the Arabic sentence into Spanish.
أصبحت لأفهم وجهة نظر "" جورج بيرنز "" ، الذي كان ما يزال يعمل في لاس فيغاس بشكل جسد وهو في التسعين.
Output:
|
Llegué a comprender los sentimientos de George Burns, que aún actuaba en Las Vegas bien entrado en sus 90 años.
|
task1232_ted_translation_ar_es
|
NIv2
|
zs_opt
| 1
|
train
|
Detailed Instructions: You are given a sentence in Arabic. Your job is to translate the Arabic sentence into Spanish.
Q: و لا واحد من هذه الخلايا مدرك و لا واحد من هذه الخلايا يعرف من أنت أو يهتم
A:
|
Ninguna de esas células es consciente, ninguna de esas células sabe quien eres tú, ni le importa.
|
task1232_ted_translation_ar_es
|
NIv2
|
zs_opt
| 9
|
train
|
Part 1. Definition
You are given a sentence in Arabic. Your job is to translate the Arabic sentence into Spanish.
Part 2. Example
فالفرقة الأولى من أجهزة الحاسب سيتم تشغيلها هذا الصيف و نحن نساعد د. زنفلر في صنع استراتيجيات حول كيفية امكانية ربط الفصل مع البيت ليمتد التعليم الى بعد اليوم المدرسي.
Answer: A fines del verano estamos instalando el primer lote de computadoras y ayudando al Dr. Zullinger a desarrollar estrategias para poder conectar el aula y el hogar y extender el aprendizaje más allá del día escolar.
Explanation: The Arabic sentence is correctly translated into Spanish, because the meaning is preserved.
Part 3. Exercise
في هذه الحالة هنا, قمنا بقيادتها عن بعد, كما فعلنا دائما في البداية.
Answer:
|
En este caso aqui, lo estamos volando remotamente, como hicimos al principio.
|
task1232_ted_translation_ar_es
|
NIv2
|
fs_opt
| 7
|
train
|
Definition: You are given a sentence in Arabic. Your job is to translate the Arabic sentence into Spanish.
Input: وقدم تم توليد الطفل بعملية قيصرية لكن باربرا عانت من نقص اكسجين في الخلايا الدماغية
Output:
|
El bebé nació por cesárea, pero Barbara sufría de anoxia cerebral una falta de oxígeno en el cerebro.
|
task1232_ted_translation_ar_es
|
NIv2
|
zs_opt
| 2
|
train
|
You are given a sentence in Arabic. Your job is to translate the Arabic sentence into Spanish.
Q: وأخبرتها أنه بعد الزرع ، جميع الدم المتدفق في أوردتها سيكون دمي ، المصنوع من خلايا النخاع الخاصة بي ، وداخل نواة كل خلية من تلك هو مجموعة كاملة من الحمض النووي الخاص بي.
A:
|
Y le dije que después del trasplante, toda la sangre que fluyera en sus venas sería mi sangre, a partir de mis células de la médula, y que dentro del núcleo de cada una de esas células habría un conjunto completo de mi ADN.
|
task1232_ted_translation_ar_es
|
NIv2
|
zs_opt
| 4
|
train
|
You are given a sentence in Arabic. Your job is to translate the Arabic sentence into Spanish.
Example: فالفرقة الأولى من أجهزة الحاسب سيتم تشغيلها هذا الصيف و نحن نساعد د. زنفلر في صنع استراتيجيات حول كيفية امكانية ربط الفصل مع البيت ليمتد التعليم الى بعد اليوم المدرسي.
Example solution: A fines del verano estamos instalando el primer lote de computadoras y ayudando al Dr. Zullinger a desarrollar estrategias para poder conectar el aula y el hogar y extender el aprendizaje más allá del día escolar.
Example explanation: The Arabic sentence is correctly translated into Spanish, because the meaning is preserved.
Problem: وتعلمون ، في الأساس ، القراءة شيء جيد ؛ تحصلون على حقائق.
|
Solution: Ya saben, básicamente, leer es algo bueno; obtienes datos.
|
task1232_ted_translation_ar_es
|
NIv2
|
fs_opt
| 5
|
test
|
Q: You are given a sentence in Arabic. Your job is to translate the Arabic sentence into Spanish.
(تصفير) ♫ قال لي واعظي ♫ ♫ لا يجب عليك أن تقلقي ♫ ♫ لأن العلماء مخطئون ♫ ♫ إذاً ، من يأبه إذا كان الوقت شتاء هنا ؟ ♫ ♫ مادمت أرتدي صديريتي الصيفية ♫ ♫ أرتدي صديريتي الصيفية ♫ ♫ كل شخص بالخارج ، خلال مانهاتن السعيدة في يناير ♫ (تصفيق) كريس أندرسن: جيل سيبيول
A:
|
(Silbando) ♫ MI predicador dijo, ♫ ♫ No te preocupes ♫ ♫ Los científicos están muy equivocados ♫ ♫ Entonces, ¿a quién le importa aquí sea invierno? ♫ Y tengo puesto mi vestido de cuello halter ♫ ♫ Tengo puesto mi vestido de cuello halter ♫ Todo el mundo salió en el feliz Manhattan en enero (Aplausos) Chris Anderson: ¡Jill Sobule!
|
task1232_ted_translation_ar_es
|
NIv2
|
zs_opt
| 7
|
validation
|
Teacher:In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Teacher: Now, understand the problem? Solve this instance: We used the Twitter API to gather real-time tweets from September 2018 until February 2019, selecting the ones containing any of the 51 Hatebase terms that are more common in hate speech tweets, as studied in BIBREF9.
Question: How is data collected, manual collection or Twitter api?
Student:
|
Twitter API
|
task460_qasper_answer_generation
|
NIv2
|
zs_opt
| 6
|
train
|
Instructions: In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Input: The text and image encodings were combined by concatenation, which resulted in a feature vector of 4,864 dimensions. This multimodal representation was afterward fed as input into a multi-layer perceptron (MLP) with two hidden layer of 100 neurons with a ReLU activation function.
Question: Is the dataset multimodal?
Output:
|
Yes
|
task460_qasper_answer_generation
|
NIv2
|
zs_opt
| 3
|
train
|
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Stanford - Twitter Sentiment Corpus (STS Corpus): STS Corpus contains 1,600K training tweets collected by a crawler from BIBREF0 . BIBREF0 constructed a test set manually with 177 negative and 182 positive tweets. The Stanford test set is small. However, it has been widely used in different evaluation tasks BIBREF0 BIBREF5 BIBREF13 .
Sanders - Twitter Sentiment Corpus: This dataset consists of hand-classified tweets collected by using search terms: INLINEFORM0 , #google, #microsoft and #twitter. We construct the dataset as BIBREF14 for binary classification.
Health Care Reform (HCR): This dataset was constructed by crawling tweets containing the hashtag #hcr BIBREF15 . Task is to predict positive/negative tweets BIBREF14 .
Question: Which datasets did they use?
Stanford - Twitter Sentiment Corpus (STS Corpus) Sanders - Twitter Sentiment Corpus Health Care Reform (HCR)
For capturing facial presence, we rely on BIBREF56 's approach that uses multilevel convolutional coarse-to-fine network cascade to tackle facial landmark localization. Facial Expression:
Following BIBREF8 's approach, we adopt Ekman's model of six emotions: anger, disgust, fear, joy, sadness and surprise, and use the Face++ API to automatically capture them from the shared images. General Image Features:
The importance of interpretable computational aesthetic features for studying users' online behavior has been highlighted by several efforts BIBREF55 , BIBREF8 , BIBREF57 . Qualitative Language Analysis: The recent LIWC version summarizes textual content in terms of language variables such as analytical thinking, clout, authenticity, and emotional tone. It also measures other linguistic dimensions such as descriptors categories (e.g., percent of target words gleaned by dictionary, or longer than six letters - Sixltr) and informal language markers (e.g., swear words, netspeak), and other linguistic aspects (e.g., 1st person singular pronouns.)
Question: What types of features are used from each data type?
facial presence Facial Expression General Image Features textual content analytical thinking clout authenticity emotional tone Sixltr informal language markers 1st person singular pronouns
For the first task, the human raters are asked to evaluate these outputs on the first three aspects, relevance, attractiveness, and language fluency on a Likert scale from 1 to 10 (integer values). For attractiveness, annotators are asked how attractive the headlines are.
Question: How is attraction score measured?
|
annotators are asked how attractive the headlines are Likert scale from 1 to 10 (integer values)
|
task460_qasper_answer_generation
|
NIv2
|
fs_opt
| 0
|
train
|
Instructions: In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Input: Our experiments are run using the English$\rightarrow $Italian/German portions of the MuST-C corpus BIBREF25, which is extracted from TED talks, using the same train/validation/test split as provided with the corpus (see Table TABREF18). As additional data, we use a mix of public and proprietary data for about 16 million sentence pairs for English-Italian (En-It) and $4.4$ million WMT14 sentence pairs for the English-German (En-De).
Question: What dataset do they use?
Output:
|
English$\rightarrow $Italian/German portions of the MuST-C corpus As additional data, we use a mix of public and proprietary data for about 16 million sentence pairs for English-Italian (En-It) and $4.4$ million WMT14 sentence pairs for the English-German (En-De)
|
task460_qasper_answer_generation
|
NIv2
|
zs_opt
| 3
|
train
|
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Input: Consider Input: In its general form, a type-logical grammar consists of following components:
Question: Does Grail accept Prolog inputs?
Output: No
Input: Consider Input: We use standard Precision, Recall and F1 at mention level (Micro) and at the document level (Macro) as measurements. We can see the average linking precision (Micro) of WW is lower than that of TAC2010, and NCEL outperforms all baseline methods in both easy and hard cases.
Question: How do they verify generalization ability?
Output: By calculating Macro F1 metric at the document level.
Input: Consider Input: For model distillation BIBREF6 , we extract sentences from Wikipedia in languages for which public multilingual is pretrained. For each sentence, we use the open-source BERT wordpiece tokenizer BIBREF4 , BIBREF1 and compute cross-entropy loss for each wordpiece: INLINEFORM0
where INLINEFORM0 is the cross-entropy function, INLINEFORM1 is the softmax function, INLINEFORM2 is the BERT model's logit of the current wordpiece, INLINEFORM3 is the small BERT model's logits and INLINEFORM4 is a temperature hyperparameter, explained in Section SECREF11 .
To train the distilled multilingual model mMiniBERT, we first use the distillation loss above to train the student from scratch using the teacher's logits on unlabeled data. Afterwards, we finetune the student model on the labeled data the teacher is trained on.
Question: How do they compress the model?
|
Output: we extract sentences from Wikipedia in languages for which public multilingual is pretrained. For each sentence, we use the open-source BERT wordpiece tokenizer BIBREF4 , BIBREF1 and compute cross-entropy loss for each wordpiece: INLINEFORM0
|
task460_qasper_answer_generation
|
NIv2
|
fs_opt
| 2
|
train
|
Detailed Instructions: In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
See one example below:
Problem: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chinese Short Text Summarization Dataset (LCSTS) is constructed by BIBREF1 . The dataset consists of more than 2.4 million text-summary pairs in total, constructed from a famous Chinese social media microblogging service Weibo.
Question: Are results reported only for English data?
Solution: No
Explanation: Based on the context, the dataset is constructed from a famous Chinese social media microblogging service Weibo.
Problem: Human Judgments
Following BIBREF11 , BIBREF12 and the vast amount of previous work on semantic similarity, we ask nine undergraduate subjects to assess the similarity of 360 pairs of relations from a subset of Wikidata BIBREF8 that are chosen to cover from high to low levels of similarity. In our experiment, subjects were asked to rate an integer similarity score from 0 (no similarity) to 4 (perfectly the same) for each pair.
Question: How do they gather human judgements for similarity between relations?
Solution:
|
By assessing similarity of 360 pairs of relations from a subset of Wikidata using an integer similarity score from 0 to 4
|
task460_qasper_answer_generation
|
NIv2
|
fs_opt
| 4
|
train
|
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Example: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chinese Short Text Summarization Dataset (LCSTS) is constructed by BIBREF1 . The dataset consists of more than 2.4 million text-summary pairs in total, constructed from a famous Chinese social media microblogging service Weibo.
Question: Are results reported only for English data?
Example solution: No
Example explanation: Based on the context, the dataset is constructed from a famous Chinese social media microblogging service Weibo.
Problem: We collected Japanese-Vietnamese parallel data from TED talks extracted from WIT3's corpus BIBREF15 .
Question: what japanese-vietnamese dataset do they use?
|
Solution: WIT3's corpus
|
task460_qasper_answer_generation
|
NIv2
|
fs_opt
| 5
|
train
|
instruction:
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
question:
Several recent works have investigated jointly training the acoustic model with a masking speech enhancement model BIBREF11, BIBREF12, BIBREF13, but these works did not evaluate their system on speech enhancement metrics. Indeed, our internal experiments show that without access to the clean data, joint training severely harms performance on these metrics.
Question: Which frozen acoustic model do they use?
answer:
a masking speech enhancement model BIBREF11, BIBREF12, BIBREF13
question:
In addition, we note that our approach can still lead to erroneous facts or even hallucinations. An interesting perspective might be to further constrain the model on the data structure in order to prevent inaccurate of even contradictory descriptions.
Question: What future possible improvements are listed?
answer:
rther constrain the model on the data structure in order to prevent inaccurate of even contradictory descriptions
question:
Conditional Random Fields (CRF) BIBREF15 have been extensively used for tasks of sequential nature. In this paper, we propose as one of the competitive baselines a CRF classifier trained with sklearn-crfsuite for Python 3.5 and the following configuration: algorithm = lbfgs; maximum iterations = 100; c1 = c2 = 0.1; all transitions = true; optimise = false. spaCy is a widely used NLP library that implements state-of-the-art text processing pipelines, including a sequence-labelling pipeline similar to the one described by strubell2017fast. spaCy offers several pre-trained models in Spanish, which perform basic NLP tasks such as Named Entity Recognition (NER). In this paper, we have trained a new NER model to detect NUBes-PHI labels. As the simplest baseline, a sensitive data recogniser and classifier has been developed that consists of regular-expressions and dictionary look-ups. For each category to detect a specific method has been implemented. For instance, the Date, Age, Time and Doctor detectors are based on regular-expressions; Hospital, Sex, Kinship, Location, Patient and Job are looked up in dictionaries. The dictionaries are hand-crafted from the training data available, except for the Patient's case, for which the possible candidates considered are the 100 most common female and male names in Spain according to the Instituto Nacional de Estadística (INE; Spanish Statistical Office).
Question: What are the other algorithms tested?
answer:
|
NER model CRF classifier trained with sklearn-crfsuite classifier has been developed that consists of regular-expressions and dictionary look-up
|
task460_qasper_answer_generation
|
NIv2
|
fs_opt
| 9
|
train
|
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Given the full text of a scientific publication, we want to rank its citations according to the author's judgments. We collected recent publications from the open-access PLoS journals and asked the authors to rank by closeness five citations we selected from their paper.
Question: what crowdsourcing platform is used?
|
asked the authors to rank by closeness five citations we selected from their paper
|
task460_qasper_answer_generation
|
NIv2
|
zs_opt
| 0
|
test
|
Teacher:In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Teacher: Now, understand the problem? Solve this instance: As this corpus of annotated patient notes comprises original healthcare data which contains protected health information (PHI) per The Health Information Portability and Accountability Act of 1996 (HIPAA) BIBREF16 and can be joined to the MIMIC-III database, individuals who wish to access to the data must satisfy all requirements to access the data contained within MIMIC-III. To satisfy these conditions, an individual who wishes to access the database must take a “Data or Specimens Management” course, as well as sign a user agreement, as outlined on the MIMIC-III database webpage, where the latest version of this database will be hosted as “Annotated Clinical Texts from MIMIC” BIBREF17. This corpus can also be accessed on GitHub after completing all of the above requirements.
Question: Is this dataset publicly available for commercial use?
Student:
|
No
|
task460_qasper_answer_generation
|
NIv2
|
zs_opt
| 6
|
validation
|
Given the task definition and input, reply with output. In this task, we ask you to write an answer to a question that involves event "frequency", which refers to how often an event is likely to be repeated. For example, "taking showers" typically occurs ~5 times a week, "going to Saturday market" usually happens every few weeks/months, etc. Note that a lot of the questions could have more than one correct answer. We only need a single most-likely answer. Please try to keep your "answer" as simple as possible. Concise and simple "answer" is preferred over those complex and verbose ones.
Sentence: The properties of matter depend on the types of atoms that matter is made of.
Question: How often are new atoms created?
|
each millisecond.
|
task016_mctaco_answer_generation_frequency
|
NIv2
|
zs_opt
| 5
|
train
|
In this task, we ask you to write an answer to a question that involves event "frequency", which refers to how often an event is likely to be repeated. For example, "taking showers" typically occurs ~5 times a week, "going to Saturday market" usually happens every few weeks/months, etc. Note that a lot of the questions could have more than one correct answer. We only need a single most-likely answer. Please try to keep your "answer" as simple as possible. Concise and simple "answer" is preferred over those complex and verbose ones.
Sentence: We may promise "justice for all," but for those who can't afford a lawyer, that promise is often a lie.
Question: How often can people expect to have justice?
|
always.
|
task016_mctaco_answer_generation_frequency
|
NIv2
|
zs_opt
| 0
|
train
|
Definition: In this task, we ask you to write an answer to a question that involves event "frequency", which refers to how often an event is likely to be repeated. For example, "taking showers" typically occurs ~5 times a week, "going to Saturday market" usually happens every few weeks/months, etc. Note that a lot of the questions could have more than one correct answer. We only need a single most-likely answer. Please try to keep your "answer" as simple as possible. Concise and simple "answer" is preferred over those complex and verbose ones.
Input: Sentence: A majority of 65 votes in the 128-member body was required to reject his reinstatement.
Question: How often are elections held?
Output:
|
every four years.
|
task016_mctaco_answer_generation_frequency
|
NIv2
|
zs_opt
| 2
|
train
|
In this task, we ask you to write an answer to a question that involves event "frequency", which refers to how often an event is likely to be repeated. For example, "taking showers" typically occurs ~5 times a week, "going to Saturday market" usually happens every few weeks/months, etc. Note that a lot of the questions could have more than one correct answer. We only need a single most-likely answer. Please try to keep your "answer" as simple as possible. Concise and simple "answer" is preferred over those complex and verbose ones.
Sentence: Technologically and sociologically, it was a phenomenal breakthrough: with fire, communities could live year 'round in one cave, in which cooking and even smelting could be pursued.
Question: How often did the communities use the fire?
every night.
Sentence: They practiced a slash-and-burn agriculture of yams and millet, a technique that exhausted the soil and imposed a semi-nomadic existence from one jungle clearing to another.
Question: How often does the group move?
around once a year.
Sentence: They are able to study the DNA of the organism that no longer lives on Earth.
Question: How often do they study the DNA of extinct organisms?
|
every year.
|
task016_mctaco_answer_generation_frequency
|
NIv2
|
fs_opt
| 0
|
train
|
Given the task definition, example input & output, solve the new input case.
In this task, we ask you to write an answer to a question that involves event "frequency", which refers to how often an event is likely to be repeated. For example, "taking showers" typically occurs ~5 times a week, "going to Saturday market" usually happens every few weeks/months, etc. Note that a lot of the questions could have more than one correct answer. We only need a single most-likely answer. Please try to keep your "answer" as simple as possible. Concise and simple "answer" is preferred over those complex and verbose ones.
Example: Sentence: Jack played basketball after school, after which he was very tired.
Question: How often does Jack play basketball?
Output: twice a week.
"Playing basketball" normally occurs few times a week.
New input case for you: Sentence: The scout ship re-emerged from the hole - a brilliant speck emerging from a sphere of velvety blackness.
Question: How many times did the scout ship re-emerge from the hole?
Output:
|
once.
|
task016_mctaco_answer_generation_frequency
|
NIv2
|
fs_opt
| 1
|
train
|
In this task, we ask you to write an answer to a question that involves event "frequency", which refers to how often an event is likely to be repeated. For example, "taking showers" typically occurs ~5 times a week, "going to Saturday market" usually happens every few weeks/months, etc. Note that a lot of the questions could have more than one correct answer. We only need a single most-likely answer. Please try to keep your "answer" as simple as possible. Concise and simple "answer" is preferred over those complex and verbose ones.
Ex Input:
Sentence: Chernow notes how the membership soon included many of Hamilton's friends and associates.
Question: How often do Hamilton's friends use their membership?
Ex Output:
every week.
Ex Input:
Sentence: They also liked to go through the small forest behind the house, making a game of hide and seek.
Question: How often do they play hide and seek?
Ex Output:
they play hide and seek every day.
Ex Input:
Sentence: At NEADS, the report of the hijacking was relayed immediately to Battle Commander Colonel Robert Marr.
Question: How often had there been threats?
Ex Output:
|
several times.
|
task016_mctaco_answer_generation_frequency
|
NIv2
|
fs_opt
| 1
|
train
|
Definition: In this task, we ask you to write an answer to a question that involves event "frequency", which refers to how often an event is likely to be repeated. For example, "taking showers" typically occurs ~5 times a week, "going to Saturday market" usually happens every few weeks/months, etc. Note that a lot of the questions could have more than one correct answer. We only need a single most-likely answer. Please try to keep your "answer" as simple as possible. Concise and simple "answer" is preferred over those complex and verbose ones.
Input: Sentence: Technologically and sociologically, it was a phenomenal breakthrough: with fire, communities could live year 'round in one cave, in which cooking and even smelting could be pursued.
Question: How often did the communities use the fire?
Output:
|
every night.
|
task016_mctaco_answer_generation_frequency
|
NIv2
|
zs_opt
| 2
|
train
|
Teacher:In this task, we ask you to write an answer to a question that involves event "frequency", which refers to how often an event is likely to be repeated. For example, "taking showers" typically occurs ~5 times a week, "going to Saturday market" usually happens every few weeks/months, etc. Note that a lot of the questions could have more than one correct answer. We only need a single most-likely answer. Please try to keep your "answer" as simple as possible. Concise and simple "answer" is preferred over those complex and verbose ones.
Teacher: Now, understand the problem? Solve this instance: Sentence: Casino operators had to reassess the nature of their business.
Question: How many days a week do the casino operators go to work?
Student:
|
they go to work 5 days a week.
|
task016_mctaco_answer_generation_frequency
|
NIv2
|
zs_opt
| 6
|
train
|
In this task, we ask you to write an answer to a question that involves event "frequency", which refers to how often an event is likely to be repeated. For example, "taking showers" typically occurs ~5 times a week, "going to Saturday market" usually happens every few weeks/months, etc. Note that a lot of the questions could have more than one correct answer. We only need a single most-likely answer. Please try to keep your "answer" as simple as possible. Concise and simple "answer" is preferred over those complex and verbose ones.
Sentence: When Mary called Max's name he left the squirrel and happily returned to Mary, wagging his tail as he went.
Question: How often does Max play with squirrels?
every week.
Sentence: A lot of poor people are not going to get served,'' she said.
Question: How often are the poor turned away for services?
every day.
Sentence: The fossils that are very distinct at certain times of Earths history are called index fossils.
Question: How many times a year does the average user go to a museum to look at fossils?
|
once.
|
task016_mctaco_answer_generation_frequency
|
NIv2
|
fs_opt
| 0
|
test
|
Teacher: In this task, we ask you to write an answer to a question that involves event "frequency", which refers to how often an event is likely to be repeated. For example, "taking showers" typically occurs ~5 times a week, "going to Saturday market" usually happens every few weeks/months, etc. Note that a lot of the questions could have more than one correct answer. We only need a single most-likely answer. Please try to keep your "answer" as simple as possible. Concise and simple "answer" is preferred over those complex and verbose ones.
Teacher: Now, understand the problem? If you are still confused, see the following example:
Sentence: Jack played basketball after school, after which he was very tired.
Question: How often does Jack play basketball?
Solution: twice a week.
Reason: "Playing basketball" normally occurs few times a week.
Now, solve this instance: Sentence: But Judge Thornton declared in court, "When these orders are entered, you don't just do whatever you damn well please and ignore them.".
Question: How often does the judge scold someone in court?
Student:
|
every day.
|
task016_mctaco_answer_generation_frequency
|
NIv2
|
fs_opt
| 2
|
validation
|
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